Poor sleep quality is rapidly being recognized as a major health problem in that it results in non-restorative sleep with daytime fatigue, decreased cognitive function, excessive daytime sleepiness and increased risk of industrial, driving and recreational accidents. In addition, it is now becoming clear that poor sleep is a risk factor for development of hypertension (and its cardiovascular complications), as well as for the development of diabetes and depression and, possibly also, cognitive disorders such as Alzheimer's disease and other types of dementia.
The most reliable method of evaluating sleep quality is to conduct a sleep study in a specialized laboratory or in the home where electrodes are attached to the head of a subject to monitor the subject's brain activity (i.e. the electroencephalography (EEG) signal). The EEG signal is then analyzed either manually by a trained technologist or by an automated system. Sleep quality is evaluated by a number of parameters derived primarily from the EEG signal, and to a lesser extent from other recorded signals such as changes in heart rate, muscle tone and breathing. The parameters used clinically to evaluate sleep quality include total sleep time, sleep efficiency, times in different sleep stages, and importantly the frequency of arousals.
Arousals are temporary changes in the sleeping EEG signal pattern towards an awake EEG signal pattern. The standard definition of arousal by the American Academy of Sleep Medicine (AASM) is “an abrupt shift in EEG to a higher frequency, including alpha, theta or beta, for at least 3 seconds, with at least 10 seconds of stable sleep preceding the change” (Iber C. et al. The AASM Manual for the Scoring of Sleep and Associated Events. American Academy of Sleep Medicine, Westchester, Ill., 2007). However, EEG signal changes that meet this definition cover a very wide range of visual appearances, ranging from changes that barely meet the scoring criteria to very intense changes associated with very high amplitude beta waves. FIG. 1 shows examples of EEG signals comprising arousals that differ greatly in their visual intensity. In panels A to D of FIG. 1, an arousal begins near the middle of each EEG signal. In panel A, the increase in EEG signal frequency is subtle while in panel D the change in visual appearance of the EEG signal is quite gross, with intermediate changes in the visual appearance of the EEG signals being shown in middle panels B and C. In the current state of the art, arousals, whether scored manually or by an automated system, are treated equally. They are simply counted without any regard to their intensity.
There is evidence that the visual intensity of arousals is correlated with the magnitude of physiological changes that accompany arousals. Thus, Younes reported that the visual intensity of arousals (classified into four (4) levels) correlated with the magnitude of the ventilatory overshoot that follows obstructive events in obstructive sleep apnea patients (Role of arousals in the pathogenesis of obstructive sleep apnea. Am J Respir Crit Care Med 2004; 169:623-33). Also, Sforza et al. found that heart rate increased more in arousals associated with movement (Cardiac activation during arousal in humans: further evidence for hierarchy in the arousal response. Clinical Neurophysiology 2000; 111:1611-9). Thus, it is possible that scoring the intensity of arousals may provide additional guidance into which patients with sleep disorders will develop cognitive and/or cardiovascular complications.
As will be appreciated, visual scoring of arousal intensity to assign values to arousals within a scale is very time consuming and, because of its subjective nature, prone to much inter-scorer variability. In order to test the clinical significance of arousal intensity in an efficient and accurate manner, a need exists to improve arousal intensity scoring. It is therefore an object to provide a novel method, non-transitory computer readable medium and apparatus for arousal intensity scoring.